However, for the smallest datasets (i.e. Stanford-Cars, ClipArt, Sketch and Paintings), we found that stronger data augmentation and more aggressive masking prevents early overfitting. In particular, we use a uniform masking of 75% (like in the work by He et al. [1]}), as well as using random greyscale, solarization, Gaussian blur and color jittering as additional forms of data augmentation.
